International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B3. Istanbul 2004
separately. In case one of them exceed the similarity threshold, and fixed from its corresponding 3-D roof-edges. The second
they are considered as a conjugate line and a further space step is to define the shape of a rooftop according to the height of
intersection is applied to get a 3D roof-edge. the independent edges. If more than two independent edges
exist and are sufficient to fit into a planar face, then
2.3 3D Building Modelling least-squares coplanar fitting can be applied. Otherwise, the
system will provide the most possible solution by
consecutive-coplanar analysis, which is used to find a possible
planar rooftop using consecutive line-segments or any two
non-consecutive but coplanar ones.
In this section, the SMS algorithm (Rau & Chen, 2003b) for 3D
building modelling is illustrated. In the real world, it is difficult
to describe all types of buildings using a single comprehensive
building model database. In our approach, a building model
may be decomposed into several planar roof-primitives. A
roof-primitive may be a part or a complete building. Each
roof-primitive is a planar rooftop, (e.g. a horizontal or oblique
plane), with its boundary projected onto the ground as a polygon.
One roof-primitive, or a combination of roof-primitives, can be
reformed as a polyhedral building model.
The proposed SMS method is designed to have some
In the visual inspection stage, each generated roof-primitive is
examined both on the ground view and 3-D view. The user can
select the roof-primitive of interest on the ground view. The
selected | roof-primitive together with the original 3-D
roof-edges will be shown in the 3-D view. Whenever the
selected roof-primitive doest not fit the original roof-edges,
reshaping is necessary. The system will provide all possible
solutions by the consecutive-coplanar analysis and show them
on the screen one by one. The operator can thus choose the
correct one by comparing the original stereo-pair.
The primary data source for 3D building modelling using the
SMS algorithm is the 3D roof-edges. The 3D roof-edges may
come from totally manual stereo measurement using expensive
stereographic equipment or comes from a semi-automatic way
as described in the previous section that adopts the popular
graphic card only. 3. CASE STUDY
The key for the realization of 3D building modelling using the
SMS algorithm is to create an initial building model, which is
the first roof-primitive with a known topology. The initial
building model is simply built in such a way that an operator
needs only to specify the Area Of Interest (AOI) with a polygon.
By the incorporation of a reasonable height, a volumetric
In this section, a set of manually measured visible roof-edges
and three cases of semi-automatic measured roofs are evaluated.
The first one is to demonstrate the feasibility and power of the
proposed SMS method. The second one illustrates the potential
of the proposed interactive scheme for 3D building modelling.
representation of the initial building model, which covers all of 3.1 Manual Measurement of Visible Roof-Edges
the 3-D roof-edges in a process. Briefly speaking, the creation
of initial building model has the following two important The first test data set was measured manually using a DPW. The
meanings: (1) the first building model with a known topology, original data is a four-views aerial photo, which is located at the
and (2) the selection of working line-segments. Fu-Zen University, TAIWAN, as shown in figure 3. The fly
height is 1,700 meters with a scale of 1:5,000 and a ground
A pre-processing of the input 3D roof-edges is necessary before sampling distance around 12.5 cm.
modelling. The reasons are one refers to the adjustment of
geometric irregularities due to stereo measurement errors, the
other to obtaining a topology error-free solution. Many
geometric irregularities due to the errors of manual stereo
measurement can happen, such as: (1) two collinear lines are
misaligned, (2) rectangular buildings are skewed, (3) two
consecutive line-segments intersect and cause overshooting, and
(4) gaps due to image occlusions, especially in a densely
built-up area, may cause incorrect modelling. These kinds of
situations should be solved before building modelling. Since the
roof-edges are defined in the 3-D object space, these
pre-processing steps are also performed in the object space.
The SPLIT and MERGE processes can sequentially reconstruct
the topology between two consecutive line-segments and then
reform the areas as enclosed regions. In splitting, one
line-segment is chosen as a reference. If any roof-primitives
contain this roof-edge, we SPLIT them into two. For successive
roof-edges, a combination of the possible roof-primitives is
constructed. The splitting action is similar to the manual
inference of hidden corners. The merging procedure is also
worked on the 2-D horizontal plane. Every two connected
roof-primitives are analyzed successively. If the boundary
shared between them does not correspond to any 3-D roof-edges,
the two roof-primitives will be merged into one. The SHAPE
process is worked in 3-D object space. The first step of shaping The content of this data set can be abstractly categorized into
is to assign a possible height for each roof-edge from its three parts. One is the university campus. In which the buildings
corresponding 3-D roof-edge. Every roof-edge is automatically are large with complex boundary, and are separated to each
labeled as a shared edge or an independent edge at first. The other with a distance. The second category is a high-density
height information for an independent edge can then be assigned built-up area with groups of connected and rectangular
586
post-processing functions for visual inspection and modification.
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